Deng Haiyi, Yang Yi, Yang Yilin, Liang Ying, Wang Fei, Yang Lanmengxi, Ma Kangjing, Mo Junyi, Chenli Zekun, Wu Junwei, Liu Yuheng, Su Jin, Wang Liqiang, Su Shiyu, Xia Yinxiao, Wang Zirui, Wu Xinyi, Sun Ni, Guan Wenhui, Lin Xinqing, Xie Xiaohong, Liao Yao, Zhou Chengzhi, Wang Lifu
KingMed School of Laboratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China.
State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
J Immunother Cancer. 2025 Sep 12;13(9):e012270. doi: 10.1136/jitc-2025-012270.
Checkpoint inhibitor-related pneumonitis (CIP) represents a highly lethal immune-related adverse event. Early diagnosis of CIP is crucial for timely intervention and improved prognosis; however, the absence of precise and effective diagnostic techniques often leads to underdiagnosis and misdiagnosis. This study aims to identify microRNA (miRNA) features from serum and extracellular vesicles (EVs) for the early CIP detection and prognosis.
Small RNA sequencing identified candidate miRNAs in 27 serum-derived EV samples from persons with lung cancer and CIP (CIP group) and those without, including immunotherapy-treated persons with lung cancer without CIP (immune checkpoint inhibitor, ICI group) and patients with infectious pneumonia (PNE group). These miRNAs were validated in EV samples in a discovery cohort (n=48) using a quantitative reverse transcription-PCR (qRT-PCR). Diagnostic models for the biomarkers were developed using a training cohort (ICI:47, PNE:28, CIP:31) and validated in a separate validation cohort (ICI:32, PNE:19, CIP:21) using qRT-PCR in both EV and serum samples, and logistic regression. Using a Cox regression model, we built a prognostic risk stratification for patients with CIP based on three miRNAs.
Sequencing analysis initially screened and identified 13 overexpressed miRNAs in patients with CIP. Subsequently, qRT-PCR demonstrated that three miRNAs (EVs miR-193a-5p, serum miR-193a-5p, and serum miR-378a-3p) effectively distinguished CIP from non-CIP individuals (training cohort: area under the curve (AUC)=0.870; validation cohort: AUC=0.837). Notably, this miRNA signature was equally robust in differentiating CIP from ICI (training cohort: AUC=0.823; validation cohort: AUC=0.845) and PNE groups (training cohort: AUC=0.892; validation cohort: AUC=0.907). Furthermore, when combined with lymphocyte levels, the miRNA signature significantly enhanced the overall diagnostic accuracy in distinguishing CIP from the non-CIP group (training cohort: AUC=0.900; validation cohort: AUC=0.932), and maintained its robustness in distinguishing CIP from the ICI group (training cohort: AUC=0.898; validation cohort: AUC=0.946) and the PNE group (training cohort: AUC=0.938; validation cohort: AUC=0.959). Additionally, the three-miRNA panel was independently and significantly associated with overall survival in patients with CIP (HR: 2.827; p=0.040).
Our circulating miRNA-based signature represents a non-invasive and robust diagnostic tool for patients with CIP and could accurately predict their prognosis. This signature may facilitate early detection and personalized management of these patients.
检查点抑制剂相关肺炎（CIP）是一种极具致死性的免疫相关不良事件。CIP的早期诊断对于及时干预和改善预后至关重要；然而，缺乏精确有效的诊断技术常常导致漏诊和误诊。本研究旨在从血清和细胞外囊泡（EVs）中鉴定微小RNA（miRNA）特征，用于CIP的早期检测和预后评估。
通过小RNA测序在27份来自肺癌合并CIP患者（CIP组）以及无CIP患者的血清来源EV样本中鉴定候选miRNA，后者包括接受免疫治疗但无CIP的肺癌患者（免疫检查点抑制剂，ICI组）和感染性肺炎患者（PNE组）。这些miRNA在一个发现队列（n = 48）的EV样本中使用定量逆转录PCR（qRT-PCR）进行验证。使用一个训练队列（ICI：47例，PNE：28例，CIP：31例）开发生物标志物的诊断模型，并在一个单独的验证队列（ICI：32例，PNE：19例，CIP：21例）中对EV和血清样本使用qRT-PCR及逻辑回归进行验证。我们基于三种miRNA为CIP患者构建了一个Cox回归模型的预后风险分层。
测序分析最初筛选并鉴定出CIP患者中13种过表达的miRNA。随后，qRT-PCR表明三种miRNA（EVs miR-193a-5p、血清miR-193a-5p和血清miR-378a-3p）能有效区分CIP患者与非CIP个体（训练队列：曲线下面积（AUC）= 0.870；验证队列：AUC = 0.837）。值得注意的是，这种miRNA特征在区分CIP与ICI（训练队列：AUC = 0.823；验证队列：AUC = 0.845）以及PNE组（训练队列：AUC = 0.892；验证队列：AUC = 0.907）时同样有效。此外，当与淋巴细胞水平相结合时，该miRNA特征显著提高了区分CIP与非CIP组的总体诊断准确性（训练队列：AUC = 0.900；验证队列：AUC = 0.932），并在区分CIP与ICI组（训练队列：AUC = 0.898；验证队列：AUC = 0.946）以及PNE组（训练队列：AUC = 0.938；验证队列：AUC = 0.959）时保持其有效性。此外，这三种miRNA组成的panel与CIP患者的总生存期独立且显著相关（风险比：2.827；p = 0.040）。
我们基于循环miRNA的特征代表了一种针对CIP患者的非侵入性且有效的诊断工具，并且能够准确预测其预后。这一特征可能有助于这些患者的早期检测和个性化管理。
